Reduced order modeling for accelerated Monte Carlo simulations in radiation transport

Indika Udagedara, Brian Helenbrook, Aaron Luttman and Stephen E. Mitchell

Applied Mathematics and Computation, 2015, vol. 267, issue C, 237-251

Abstract: Large-scale Monte Carlo simulations are the most common computational approach for modeling radiation transport, but, in some applications, the time required for such simulations can make them impractical and the required computational resources can be prohibitive. To increase computational efficiency, there are a wide range of techniques that are designed to reduce the number of so-called particle histories that must simulated to obtain statistically significant results, but it is also possible to use reduced order modeling (ROM) approaches to capture the dynamics of specific transport applications, further reducing the computational requirements for accurate results. This can lead to near real-time approaches for simulating transport for certain classes of problems, and in this work we focus on the application of characterizing radiation spectra measured from weak radiation sources emitting only a small number of particles. The proper orthogonal decomposition (POD) is adapted for use with Monte Carlo simulations to generate reduced order models of terrestrial radiation detection scenarios, and we show that the use of ROMs can result in high fidelity radiation transport simulations computed with many fewer particle histories than are required for standard calculations.

Keywords: Reduced order modeling; Radiation transport; Monte Carlo simulations; Proper orthogonal decomposition (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:267:y:2015:i:c:p:237-251

DOI: 10.1016/j.amc.2015.03.113

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